{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f694259c-4307-41a7-8308-4dc1d6848b40",
   "metadata": {},
   "source": [
    "## Multiple Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9cd4279c-6c16-4988-8a9b-52c0f9858ced",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_california_housing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e95d23c1-2e1e-4729-a314-e23566c7d88b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b904512c-e3c4-40ad-875f-b912cc4ab1ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "california=fetch_california_housing()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c61e8177-75e2-4fd5-b73d-cee014dff0e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data': array([[   8.3252    ,   41.        ,    6.98412698, ...,    2.55555556,\n",
       "           37.88      , -122.23      ],\n",
       "        [   8.3014    ,   21.        ,    6.23813708, ...,    2.10984183,\n",
       "           37.86      , -122.22      ],\n",
       "        [   7.2574    ,   52.        ,    8.28813559, ...,    2.80225989,\n",
       "           37.85      , -122.24      ],\n",
       "        ...,\n",
       "        [   1.7       ,   17.        ,    5.20554273, ...,    2.3256351 ,\n",
       "           39.43      , -121.22      ],\n",
       "        [   1.8672    ,   18.        ,    5.32951289, ...,    2.12320917,\n",
       "           39.43      , -121.32      ],\n",
       "        [   2.3886    ,   16.        ,    5.25471698, ...,    2.61698113,\n",
       "           39.37      , -121.24      ]]),\n",
       " 'target': array([4.526, 3.585, 3.521, ..., 0.923, 0.847, 0.894]),\n",
       " 'frame': None,\n",
       " 'target_names': ['MedHouseVal'],\n",
       " 'feature_names': ['MedInc',\n",
       "  'HouseAge',\n",
       "  'AveRooms',\n",
       "  'AveBedrms',\n",
       "  'Population',\n",
       "  'AveOccup',\n",
       "  'Latitude',\n",
       "  'Longitude'],\n",
       " 'DESCR': '.. _california_housing_dataset:\\n\\nCalifornia Housing dataset\\n--------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 20640\\n\\n    :Number of Attributes: 8 numeric, predictive attributes and the target\\n\\n    :Attribute Information:\\n        - MedInc        median income in block group\\n        - HouseAge      median house age in block group\\n        - AveRooms      average number of rooms per household\\n        - AveBedrms     average number of bedrooms per household\\n        - Population    block group population\\n        - AveOccup      average number of household members\\n        - Latitude      block group latitude\\n        - Longitude     block group longitude\\n\\n    :Missing Attribute Values: None\\n\\nThis dataset was obtained from the StatLib repository.\\nhttps://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html\\n\\nThe target variable is the median house value for California districts,\\nexpressed in hundreds of thousands of dollars ($100,000).\\n\\nThis dataset was derived from the 1990 U.S. census, using one row per census\\nblock group. A block group is the smallest geographical unit for which the U.S.\\nCensus Bureau publishes sample data (a block group typically has a population\\nof 600 to 3,000 people).\\n\\nAn household is a group of people residing within a home. Since the average\\nnumber of rooms and bedrooms in this dataset are provided per household, these\\ncolumns may take surpinsingly large values for block groups with few households\\nand many empty houses, such as vacation resorts.\\n\\nIt can be downloaded/loaded using the\\n:func:`sklearn.datasets.fetch_california_housing` function.\\n\\n.. topic:: References\\n\\n    - Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,\\n      Statistics and Probability Letters, 33 (1997) 291-297\\n'}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "california"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "af5d2da1-0456-48d1-a40c-2f6cf8b65b4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'target_names', 'feature_names', 'DESCR'])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "california.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0d13a5b7-a939-472e-9537-bf81953004b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _california_housing_dataset:\n",
      "\n",
      "California Housing dataset\n",
      "--------------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 20640\n",
      "\n",
      "    :Number of Attributes: 8 numeric, predictive attributes and the target\n",
      "\n",
      "    :Attribute Information:\n",
      "        - MedInc        median income in block group\n",
      "        - HouseAge      median house age in block group\n",
      "        - AveRooms      average number of rooms per household\n",
      "        - AveBedrms     average number of bedrooms per household\n",
      "        - Population    block group population\n",
      "        - AveOccup      average number of household members\n",
      "        - Latitude      block group latitude\n",
      "        - Longitude     block group longitude\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "This dataset was obtained from the StatLib repository.\n",
      "https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html\n",
      "\n",
      "The target variable is the median house value for California districts,\n",
      "expressed in hundreds of thousands of dollars ($100,000).\n",
      "\n",
      "This dataset was derived from the 1990 U.S. census, using one row per census\n",
      "block group. A block group is the smallest geographical unit for which the U.S.\n",
      "Census Bureau publishes sample data (a block group typically has a population\n",
      "of 600 to 3,000 people).\n",
      "\n",
      "An household is a group of people residing within a home. Since the average\n",
      "number of rooms and bedrooms in this dataset are provided per household, these\n",
      "columns may take surpinsingly large values for block groups with few households\n",
      "and many empty houses, such as vacation resorts.\n",
      "\n",
      "It can be downloaded/loaded using the\n",
      ":func:`sklearn.datasets.fetch_california_housing` function.\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "    - Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,\n",
      "      Statistics and Probability Letters, 33 (1997) 291-297\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(california.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bbb56d45-c716-4bcf-8dc8-42a7ec3a1f91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MedHouseVal']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "california.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "943f69c6-4c5a-4965-a245-d7477421d7ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[   8.3252       41.            6.98412698 ...    2.55555556\n",
      "    37.88       -122.23      ]\n",
      " [   8.3014       21.            6.23813708 ...    2.10984183\n",
      "    37.86       -122.22      ]\n",
      " [   7.2574       52.            8.28813559 ...    2.80225989\n",
      "    37.85       -122.24      ]\n",
      " ...\n",
      " [   1.7          17.            5.20554273 ...    2.3256351\n",
      "    39.43       -121.22      ]\n",
      " [   1.8672       18.            5.32951289 ...    2.12320917\n",
      "    39.43       -121.32      ]\n",
      " [   2.3886       16.            5.25471698 ...    2.61698113\n",
      "    39.37       -121.24      ]]\n"
     ]
    }
   ],
   "source": [
    "print(california.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "73ff15fc-7b5f-4960-b528-9305b009bbb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4.526 3.585 3.521 ... 0.923 0.847 0.894]\n"
     ]
    }
   ],
   "source": [
    "print(california.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "de504dc6-b5e2-4866-abea-8389878080ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MedInc',\n",
       " 'HouseAge',\n",
       " 'AveRooms',\n",
       " 'AveBedrms',\n",
       " 'Population',\n",
       " 'AveOccup',\n",
       " 'Latitude',\n",
       " 'Longitude']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "california.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "082b14ff-5e14-43ca-bb80-2bd8df26f00f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.3252</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>37.88</td>\n",
       "      <td>-122.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.3014</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>2.109842</td>\n",
       "      <td>37.86</td>\n",
       "      <td>-122.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.2574</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>1.073446</td>\n",
       "      <td>496.0</td>\n",
       "      <td>2.802260</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.6431</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>1.073059</td>\n",
       "      <td>558.0</td>\n",
       "      <td>2.547945</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.8462</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>1.081081</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.181467</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
       "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
       "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
       "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
       "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
       "\n",
       "   Longitude  \n",
       "0    -122.23  \n",
       "1    -122.22  \n",
       "2    -122.24  \n",
       "3    -122.25  \n",
       "4    -122.25  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Lets prepare the dataframe \n",
    "dataset=pd.DataFrame(california.data,columns=california.feature_names)\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ad33ade8-2c39-4c44-a667-84a11002edba",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Price']=california.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a5e8f25b-d3f1-485c-9553-a0b9634d03ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.3252</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>37.88</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>4.526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.3014</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>2.109842</td>\n",
       "      <td>37.86</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>3.585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.2574</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>1.073446</td>\n",
       "      <td>496.0</td>\n",
       "      <td>2.802260</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>3.521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.6431</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>1.073059</td>\n",
       "      <td>558.0</td>\n",
       "      <td>2.547945</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>3.413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.8462</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>1.081081</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.181467</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>3.422</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
       "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
       "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
       "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
       "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
       "\n",
       "   Longitude  Price  \n",
       "0    -122.23  4.526  \n",
       "1    -122.22  3.585  \n",
       "2    -122.24  3.521  \n",
       "3    -122.25  3.413  \n",
       "4    -122.25  3.422  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6fd23cb1-83a5-4c37-8850-ccc903a751db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 9 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   MedInc      20640 non-null  float64\n",
      " 1   HouseAge    20640 non-null  float64\n",
      " 2   AveRooms    20640 non-null  float64\n",
      " 3   AveBedrms   20640 non-null  float64\n",
      " 4   Population  20640 non-null  float64\n",
      " 5   AveOccup    20640 non-null  float64\n",
      " 6   Latitude    20640 non-null  float64\n",
      " 7   Longitude   20640 non-null  float64\n",
      " 8   Price       20640 non-null  float64\n",
      "dtypes: float64(9)\n",
      "memory usage: 1.4 MB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2290863d-28db-44e0-b2c6-3de687549dd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MedInc        0\n",
       "HouseAge      0\n",
       "AveRooms      0\n",
       "AveBedrms     0\n",
       "Population    0\n",
       "AveOccup      0\n",
       "Latitude      0\n",
       "Longitude     0\n",
       "Price         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "68c9e9b9-4c6a-41ff-a237-750cf06fb6cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.870671</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>5.429000</td>\n",
       "      <td>1.096675</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>3.070655</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>-119.569704</td>\n",
       "      <td>2.068558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.899822</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2.474173</td>\n",
       "      <td>0.473911</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>10.386050</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>2.003532</td>\n",
       "      <td>1.153956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.499900</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>-124.350000</td>\n",
       "      <td>0.149990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.563400</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>4.440716</td>\n",
       "      <td>1.006079</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>2.429741</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>-121.800000</td>\n",
       "      <td>1.196000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.534800</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>5.229129</td>\n",
       "      <td>1.048780</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>2.818116</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>-118.490000</td>\n",
       "      <td>1.797000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.743250</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>6.052381</td>\n",
       "      <td>1.099526</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>3.282261</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>-118.010000</td>\n",
       "      <td>2.647250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.000100</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>141.909091</td>\n",
       "      <td>34.066667</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>1243.333333</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>-114.310000</td>\n",
       "      <td>5.000010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             MedInc      HouseAge      AveRooms     AveBedrms    Population  \\\n",
       "count  20640.000000  20640.000000  20640.000000  20640.000000  20640.000000   \n",
       "mean       3.870671     28.639486      5.429000      1.096675   1425.476744   \n",
       "std        1.899822     12.585558      2.474173      0.473911   1132.462122   \n",
       "min        0.499900      1.000000      0.846154      0.333333      3.000000   \n",
       "25%        2.563400     18.000000      4.440716      1.006079    787.000000   \n",
       "50%        3.534800     29.000000      5.229129      1.048780   1166.000000   \n",
       "75%        4.743250     37.000000      6.052381      1.099526   1725.000000   \n",
       "max       15.000100     52.000000    141.909091     34.066667  35682.000000   \n",
       "\n",
       "           AveOccup      Latitude     Longitude         Price  \n",
       "count  20640.000000  20640.000000  20640.000000  20640.000000  \n",
       "mean       3.070655     35.631861   -119.569704      2.068558  \n",
       "std       10.386050      2.135952      2.003532      1.153956  \n",
       "min        0.692308     32.540000   -124.350000      0.149990  \n",
       "25%        2.429741     33.930000   -121.800000      1.196000  \n",
       "50%        2.818116     34.260000   -118.490000      1.797000  \n",
       "75%        3.282261     37.710000   -118.010000      2.647250  \n",
       "max     1243.333333     41.950000   -114.310000      5.000010  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c5977aac-45d5-40b5-bd81-b811dacdcf60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MedInc</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.119034</td>\n",
       "      <td>0.326895</td>\n",
       "      <td>-0.062040</td>\n",
       "      <td>0.004834</td>\n",
       "      <td>0.018766</td>\n",
       "      <td>-0.079809</td>\n",
       "      <td>-0.015176</td>\n",
       "      <td>0.688075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HouseAge</th>\n",
       "      <td>-0.119034</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.153277</td>\n",
       "      <td>-0.077747</td>\n",
       "      <td>-0.296244</td>\n",
       "      <td>0.013191</td>\n",
       "      <td>0.011173</td>\n",
       "      <td>-0.108197</td>\n",
       "      <td>0.105623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AveRooms</th>\n",
       "      <td>0.326895</td>\n",
       "      <td>-0.153277</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.847621</td>\n",
       "      <td>-0.072213</td>\n",
       "      <td>-0.004852</td>\n",
       "      <td>0.106389</td>\n",
       "      <td>-0.027540</td>\n",
       "      <td>0.151948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AveBedrms</th>\n",
       "      <td>-0.062040</td>\n",
       "      <td>-0.077747</td>\n",
       "      <td>0.847621</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.066197</td>\n",
       "      <td>-0.006181</td>\n",
       "      <td>0.069721</td>\n",
       "      <td>0.013344</td>\n",
       "      <td>-0.046701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Population</th>\n",
       "      <td>0.004834</td>\n",
       "      <td>-0.296244</td>\n",
       "      <td>-0.072213</td>\n",
       "      <td>-0.066197</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.069863</td>\n",
       "      <td>-0.108785</td>\n",
       "      <td>0.099773</td>\n",
       "      <td>-0.024650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AveOccup</th>\n",
       "      <td>0.018766</td>\n",
       "      <td>0.013191</td>\n",
       "      <td>-0.004852</td>\n",
       "      <td>-0.006181</td>\n",
       "      <td>0.069863</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002366</td>\n",
       "      <td>0.002476</td>\n",
       "      <td>-0.023737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latitude</th>\n",
       "      <td>-0.079809</td>\n",
       "      <td>0.011173</td>\n",
       "      <td>0.106389</td>\n",
       "      <td>0.069721</td>\n",
       "      <td>-0.108785</td>\n",
       "      <td>0.002366</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.924664</td>\n",
       "      <td>-0.144160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Longitude</th>\n",
       "      <td>-0.015176</td>\n",
       "      <td>-0.108197</td>\n",
       "      <td>-0.027540</td>\n",
       "      <td>0.013344</td>\n",
       "      <td>0.099773</td>\n",
       "      <td>0.002476</td>\n",
       "      <td>-0.924664</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.045967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <td>0.688075</td>\n",
       "      <td>0.105623</td>\n",
       "      <td>0.151948</td>\n",
       "      <td>-0.046701</td>\n",
       "      <td>-0.024650</td>\n",
       "      <td>-0.023737</td>\n",
       "      <td>-0.144160</td>\n",
       "      <td>-0.045967</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  \\\n",
       "MedInc      1.000000 -0.119034  0.326895  -0.062040    0.004834  0.018766   \n",
       "HouseAge   -0.119034  1.000000 -0.153277  -0.077747   -0.296244  0.013191   \n",
       "AveRooms    0.326895 -0.153277  1.000000   0.847621   -0.072213 -0.004852   \n",
       "AveBedrms  -0.062040 -0.077747  0.847621   1.000000   -0.066197 -0.006181   \n",
       "Population  0.004834 -0.296244 -0.072213  -0.066197    1.000000  0.069863   \n",
       "AveOccup    0.018766  0.013191 -0.004852  -0.006181    0.069863  1.000000   \n",
       "Latitude   -0.079809  0.011173  0.106389   0.069721   -0.108785  0.002366   \n",
       "Longitude  -0.015176 -0.108197 -0.027540   0.013344    0.099773  0.002476   \n",
       "Price       0.688075  0.105623  0.151948  -0.046701   -0.024650 -0.023737   \n",
       "\n",
       "            Latitude  Longitude     Price  \n",
       "MedInc     -0.079809  -0.015176  0.688075  \n",
       "HouseAge    0.011173  -0.108197  0.105623  \n",
       "AveRooms    0.106389  -0.027540  0.151948  \n",
       "AveBedrms   0.069721   0.013344 -0.046701  \n",
       "Population -0.108785   0.099773 -0.024650  \n",
       "AveOccup    0.002366   0.002476 -0.023737  \n",
       "Latitude    1.000000  -0.924664 -0.144160  \n",
       "Longitude  -0.924664   1.000000 -0.045967  \n",
       "Price      -0.144160  -0.045967  1.000000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0cf55835-6098-4f13-85b6-1ae54bca5bb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.heatmap(dataset.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7624160b-54f9-460d-bd25-49cf297f21be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.3252</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>37.88</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>4.526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.3014</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>2.109842</td>\n",
       "      <td>37.86</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>3.585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.2574</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>1.073446</td>\n",
       "      <td>496.0</td>\n",
       "      <td>2.802260</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>3.521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.6431</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>1.073059</td>\n",
       "      <td>558.0</td>\n",
       "      <td>2.547945</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>3.413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.8462</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>1.081081</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.181467</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>3.422</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
       "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
       "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
       "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
       "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
       "\n",
       "   Longitude  Price  \n",
       "0    -122.23  4.526  \n",
       "1    -122.22  3.585  \n",
       "2    -122.24  3.521  \n",
       "3    -122.25  3.413  \n",
       "4    -122.25  3.422  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a8f28947-66d7-4e44-b266-8825a011fb9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Independent and Dependent features\n",
    "X=dataset.iloc[:,:-1] #independent features\n",
    "y=dataset.iloc[:,-1] #dependent features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e15e5f68-1dbf-43c4-bb62-cbdc96f04493",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.3252</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>37.88</td>\n",
       "      <td>-122.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.3014</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>2.109842</td>\n",
       "      <td>37.86</td>\n",
       "      <td>-122.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.2574</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>1.073446</td>\n",
       "      <td>496.0</td>\n",
       "      <td>2.802260</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.6431</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>1.073059</td>\n",
       "      <td>558.0</td>\n",
       "      <td>2.547945</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.8462</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>1.081081</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.181467</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
       "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
       "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
       "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
       "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
       "\n",
       "   Longitude  \n",
       "0    -122.23  \n",
       "1    -122.22  \n",
       "2    -122.24  \n",
       "3    -122.25  \n",
       "4    -122.25  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "decbcfb9-4a7d-4c48-9533-a4c969872ed5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.33, random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3d8fe5c6-8fcc-459b-b86f-976223b32f6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((13828, 8), (13828,), (6812, 8), (6812,))"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape,y_train.shape,X_test.shape,y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ec128337-aa55-48c1-a781-164904c68042",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler=StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6d4ee5c5-9484-48f5-bb63-f88b50266370",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train=scaler.fit_transform(X_train)\n",
    "X_test=scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3eb43348-8a37-42e5-b3ce-61130b0f1cd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.72986836,  1.22081889, -0.70305988, ...,  0.05861244,\n",
       "         0.96929441, -1.43979718],\n",
       "       [-0.61046678, -0.28439808,  0.07828001, ...,  0.13015917,\n",
       "        -0.75823526,  1.08204942],\n",
       "       [ 0.00784578, -0.60128586, -0.2447376 , ..., -0.09793279,\n",
       "         0.94594941, -1.2454256 ],\n",
       "       ...,\n",
       "       [ 0.88684913, -1.78961504, -0.21300658, ...,  0.09549475,\n",
       "         0.78720344, -1.10587678],\n",
       "       [-0.87672223,  0.50782138, -1.10043274, ...,  0.18513096,\n",
       "        -0.77224225,  0.66838683],\n",
       "       [-0.62742573, -0.99739558, -0.60483749, ..., -0.08418874,\n",
       "         0.77786545, -1.15073176]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "16f631d7-c65c-4fb4-aadd-f41d78987f24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.75154854, -1.31428337, -0.39376169, ...,  0.12606697,\n",
       "        -0.68820027,  0.19491761],\n",
       "       [ 0.05935857, -0.12595418, -0.33070668, ..., -0.12021013,\n",
       "         0.89459042, -1.36503888],\n",
       "       [ 0.34405687, -1.31428337, -0.41007104, ..., -0.15581759,\n",
       "        -0.91698123,  0.89764561],\n",
       "       ...,\n",
       "       [ 0.36483158,  0.27015554,  0.04216837, ..., -0.08014641,\n",
       "        -0.46875731, -0.43803598],\n",
       "       [-0.90412152, -0.91817364,  0.66736933, ..., -0.10263685,\n",
       "         2.51006411, -1.96808915],\n",
       "       [-0.43377577,  1.22081889, -0.44835491, ...,  0.2807072 ,\n",
       "        -0.74422826,  0.69330627]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "810311ad-d58b-4653-a087-48793a885ed6",
   "metadata": {},
   "source": [
    "## Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d91fa718-c553-49de-b5e2-d5cb9c8fe96f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "853c1148-597e-4987-93da-5c4ee1e82de9",
   "metadata": {},
   "outputs": [],
   "source": [
    "regression=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "77b144fc-a8a7-42d2-a226-c7849731f26b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "49b5aef7-6819-449d-86af-7ce367369350",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regression.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "9c3df1ed-65df-4627-bcd1-bc8b6d95a896",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.82872299,  0.1231163 , -0.27068752,  0.32859106,  0.00213572,\n",
       "       -0.02810091, -0.93017985, -0.89505497])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## slope or coefficients\n",
    "regression.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "97ed7277-ee77-4095-81fa-08c5e79086c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0634768086491184"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## intercepts\n",
    "regression.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d833f173-aa82-49a3-840e-49b3e6293d5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Prediction for the test data\n",
    "y_pred=regression.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "7c9e261f-b1ed-4c70-ac12-2bb5b6320138",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20303    1.674\n",
       "16966    3.541\n",
       "10623    1.602\n",
       "6146     1.408\n",
       "2208     1.078\n",
       "         ...  \n",
       "13490    1.123\n",
       "9340     3.020\n",
       "17439    1.724\n",
       "2602     0.838\n",
       "7180     1.417\n",
       "Name: Price, Length: 6812, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "220c9734-0adc-4100-a70a-8dd50db6f505",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.00397485, 2.58011486, 2.3489077 , ..., 3.09003708, 0.79152007,\n",
       "       2.04477012])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f0c7e1af-42ed-4169-9ce6-bd5defe2a949",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5522332399363619\n",
      "0.537105694300796\n",
      "0.7431239734636219\n"
     ]
    }
   ],
   "source": [
    "## Performance Metrics\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "print(mean_squared_error(y_test,y_pred))\n",
    "print(mean_absolute_error(y_test,y_pred))\n",
    "print(np.sqrt(mean_squared_error(y_test,y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "f1375ecf-d7ee-4a92-b9a0-e5f5bf52e1e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.593595852643664\n"
     ]
    }
   ],
   "source": [
    "## R square and adjusted R square\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "score=r2_score(y_test,y_pred)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "33f1e94e-95c1-4b3b-a4a7-731e29ff03c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5931179409607519"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Adjusted R square\n",
    "#display adjusted R-squared\n",
    "1 - (1-score)*(len(y_test)-1)/(len(y_test)-X_test.shape[1]-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c654d81-941f-4ac0-9020-125c16187c12",
   "metadata": {},
   "source": [
    "## Assumptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c702c246-7146-4443-b095-59e819a41220",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Test Predicted Data')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test,y_pred)\n",
    "plt.xlabel(\"Test Truth Data\")\n",
    "plt.ylabel(\"Test Predicted Data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "aa1c8339-2ffa-41c9-9124-c0a576f31f41",
   "metadata": {},
   "outputs": [],
   "source": [
    "residuals=y_test-y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "2dd2adcd-4c5f-42d8-a3cc-9b6a80015500",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20303   -1.329975\n",
       "16966    0.960885\n",
       "10623   -0.746908\n",
       "6146    -0.307462\n",
       "2208    -0.662167\n",
       "           ...   \n",
       "13490   -0.243475\n",
       "9340     0.507182\n",
       "17439   -1.366037\n",
       "2602     0.046480\n",
       "7180    -0.627770\n",
       "Name: Price, Length: 6812, dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "residuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "f2220dac-5922-4189-9f88-c560e211c987",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f32bc6527a0>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeoAAAHpCAYAAABN+X+UAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAABH+0lEQVR4nO3deXxU9b3/8ffMJJmEbBACCWgIQVAo0YrBBZBaq41F2+vSKrf2ggtYKWqLKbVSfw9r6UI3KdpbKFSB0laKLdrbhbamdQGlrRIBW3FlSwyBkASyk0lmzu+PmTPZk5nJJGcy83o+HvMAzpyZfEZD3nx3m2EYhgAAQESyW10AAADoHUENAEAEI6gBAIhgBDUAABGMoAYAIIIR1AAARDCCGgCACBZzQW0Yhurq6sTycQDAcBBzQV1fX6/09HTV19dbXQoAAP2KuaAGAGA4IagBAIhgBDUAABGMoAYAIIIR1AAARDCCGgCACEZQAwAQwQhqAAAiGEENAEAEI6gBAIhgBDUAABGMoAYAIIIR1AAARDCCGgCACEZQAwAQwQhqAAAiGEENAEAEI6gBAIhgcVYXAADRoM3tUfGBE9pz9JRONbr04LVTNTY10eqyEAUIagAIg5/tOqzv/eVt/5/zMpN131VTLKwI0YKubwAIg/8cq5UkpSV62z+HqxutLAdRhKAGgDAorW6SJF01LUuSdNT3Z2CgCGoAGCDDMHTE14K+4twxkqQjVbSoER4ENQAM0OmmVtWfaZMkXT4lU5JU3ehS3ZlWK8tClCCoAWCAjtZ4u7mz0xKVmeJUZopTUnt3ODAQBDUADNBRX7f3hNEjJEkTfb8epvsbYUBQA8AAmRPHcjN8QZ2Z7LtOUGPgCGoAGCB/UHdpUR+h6xthQFADwACZLefc0cmdfmXmN8KBoAaAATInk5kt6jxf1zctaoQDQQ0AA9DkatPJ+hZJUm6GN6DNSWVVDS2qZ4kWBoigBoABMMen05PilT4iXpKUlhiv0ckJnZ4HQkVQA8AAmEFsTiAztc/8JqgxMAQ1AAzAB6e8QZyT0Tmos9K8m55UNbQMeU2ILgQ1AAxAVYNLkjQm1dnp+sgR3q7vU02uIa8J0YWgBoABOO0L4gxfMJvMP59qJKgxMAQ1AAxAjS+IRyV3DuqRvollp5qY9Y2BIagBYADMru2MLkFt/pmubwwUQQ0AA2C2qM0WtGmUr+u7hq5vDBBBDQADYHZtd21Rm13hp+n6xgAR1AAQIo/H6HUy2ShfC5sWNQaKoAaAENWdaZXH8P5+ZNeg9rWom1vdOtPqHurSEEUIagAIkdlaTnXGKSGu84/TVGec4uw2SUwow8AQ1AAQIjOARybHd3vOZrO1b3rSyDg1Qmd5UK9du1Z5eXlKTExUQUGBdu3a1eu9t99+u2w2W7fH9OnTh7BiAPAyA7jr+LRplH8tNS1qhM7SoN62bZuWLVumhx56SHv37tXcuXM1b948lZaW9nj/Y489poqKCv+jrKxMGRkZuvnmm4e4cgCQapp63uzENIq11AgDS4N69erVWrRokRYvXqxp06ZpzZo1ysnJ0bp163q8Pz09XdnZ2f7Hnj17dOrUKd1xxx1DXDkAtG8P2m+LmpnfGADLgtrlcqmkpESFhYWdrhcWFmr37t0BvceTTz6pq6++Wrm5ub3e09LSorq6uk4PAAiH/lrU5trqGsaoMQCWBXVVVZXcbreysrI6Xc/KytLx48f7fX1FRYX+/Oc/a/HixX3et2rVKqWnp/sfOTk5A6obAExmS3nUiO6TySRO0EJ4WD6ZzGazdfqzYRjdrvVk8+bNGjlypG644YY+71uxYoVqa2v9j7KysoGUCwB+5q5kvbaoCWqEQZxVXzgzM1MOh6Nb67mysrJbK7srwzC0ceNGLViwQAkJPf8FMTmdTjmdzj7vAYBQ9DdGzQlaCAfLWtQJCQkqKChQcXFxp+vFxcWaPXt2n6996aWX9P7772vRokWDWSIA9CnQMWomk2EgLGtRS1JRUZEWLFigmTNnatasWdqwYYNKS0u1ZMkSSd5u6/Lycm3ZsqXT65588kldeumlys/Pt6JsAJDUoUXdS1AzRo1wsDSo58+fr+rqaq1cuVIVFRXKz8/Xjh07/LO4Kyoquq2prq2t1fbt2/XYY49ZUTIASJLcHkO1zd4u7a5HXJpoUSMcLA1qSVq6dKmWLl3a43ObN2/udi09PV1NTU2DXBUA9K2uuf1AjlH9rKNudLnV0uaWM84xVOUhilg+6xsAhiNzfDo1MU7xjp5/lKYlxst3LgfnUiNkBDUAhKC/8WlJstttjFNjwAhqAAhBjX+zk76XiJrd3zWMUyNEBDUAhOB0PxPJTOlJ3ufrmun6RmgIagAIQf2ZNklSamLfQW0+X+e7HwgWQQ0AIag/420hpyb2vXjGfL6eoEaICGoACEGDv0XdX1B7W9RmsAPBIqgBIAT+rm9n30GdRosaA0RQA0AI6lvMru/+xqjNoKZFjdAQ1AAQgvqgu75pUSM0BDUAhMAM3pR+ur7NIG9oIagRGoIaAELQPuub5VkYXAQ1AIQg8K5vxqgxMAQ1AITA7MpmHTUGG0ENAEFqc3vU5HJL6r/rO4111BggghoAgtRxYligk8nOtHrU6vYMal2ITgQ1AATJ7MZ2xtmVENf3j9GOQU73N0JBUANAkAI9kEOS4hx2jUhw+F5H9zeCR1ADQJDMwE3rZyKZiQllGAiCGgCCZI5RpwQc1OZaalrUCB5BDQBBCnQNtYkWNQaCoAaAIPl3JXP2P0Ytsd83BoagBoAgmduBBt71ze5kCB1BDQBBCnRXMhNnUmMgCGoACFKgB3KYUtmdDANAUANAkPyTyfrZlcxk3keLGqEgqAEgSA3M+sYQIqgBIEjB7EzW8T7WUSMUBDUABKnOP0ZNixqDj6AGgCCFujMZk8kQCoIaAIJktozZ6xtDgaAGgCAYhtFhHXVgY9Rp7EyGASCoASAIza1uuT2GpM5nTffFbFE3t7rV6vYMWm2ITgQ1AATBbBU77Db/OdP96TiW3UCrGkEiqAEgCOaEsBRnnGw2W0CviXfYlRTv8L2eoEZwCGoACIL/QI4Au71NZvc3a6kRLIIaAIIQ7K5kJvN+cyIaECiCGgCC0OQKrUWd4pv5zRg1gkVQA0AQGlvckqQRwXZ9O2lRIzQENQAEwWxRJwc449vUvukJY9QIDkENAEFodPla1AlBdn2bR13SokaQCGoACEKTL2iTncG1qM211IxRI1gENQAEIdQWNWPUCJXlQb127Vrl5eUpMTFRBQUF2rVrV5/3t7S06KGHHlJubq6cTqfOOeccbdy4cYiqBRDrGltCHaNmv2+EJrh/EobZtm3btGzZMq1du1Zz5szR+vXrNW/ePB04cEATJkzo8TW33HKLTpw4oSeffFKTJ09WZWWl2tr4xgcwNPwt6qCXZ3GCFkJjaVCvXr1aixYt0uLFiyVJa9as0V//+letW7dOq1at6nb/X/7yF7300ks6dOiQMjIyJEkTJ04cypIBxLimEFvUKf6ub2Z9IziWdX27XC6VlJSosLCw0/XCwkLt3r27x9f8/ve/18yZM/X9739fZ511ls4991wtX75czc3NvX6dlpYW1dXVdXoAQKgafcuzQm1RM0aNYFnWoq6qqpLb7VZWVlan61lZWTp+/HiPrzl06JBefvllJSYm6tlnn1VVVZWWLl2qmpqaXsepV61apW984xthrx9AbGrydX0H26JOo+sbIbJ8MlnX02cMw+j1RBqPxyObzaZf/epXuuSSS3Tttddq9erV2rx5c6+t6hUrVqi2ttb/KCsrC/tnABA7zMlkwa+jZgtRhMayFnVmZqYcDke31nNlZWW3VrZp3LhxOuuss5Senu6/Nm3aNBmGoQ8++EBTpkzp9hqn0ymn0xne4gHELLNFHfxe32x4gtBY1qJOSEhQQUGBiouLO10vLi7W7Nmze3zNnDlzdOzYMTU0NPivvfvuu7Lb7Tr77LMHtV4AkDq0qIPd8MQX7K42j1ra3GGvC9HL0q7voqIiPfHEE9q4caPeeust3X///SotLdWSJUskebutFy5c6L//1ltv1ejRo3XHHXfowIED2rlzp77yla/ozjvvVFJSklUfA0CMMAyjwxh1aFuISnR/IziWLs+aP3++qqurtXLlSlVUVCg/P187duxQbm6uJKmiokKlpaX++1NSUlRcXKz77rtPM2fO1OjRo3XLLbfoW9/6llUfAUAMcbk9avMYkoJvUTvsNiUnONTocquhpU2jUxiSQ2BshmEYVhcxlOrq6pSenq7a2lqlpaVZXQ6AYeRUo0szvukdrnv/2/MU5wiuU/LS7/xNJ+pa9Mf7Llf+Wen9vwBQBMz6BoDhwlxD7YyzBx3SUsdNT+j6RuAIagAIkH98OsgZ3yb2+0YoCGoACFD7GurgxqdNqYlsI4rgEdQAEKBQZ3yb/F3ftKgRBIIaAAIU6hpqkxnUbHqCYBDUABCggbaoGaNGKAhqAAiQ/+SsEMeo/SdoEdQIAkENAAFqahngrG+WZyEEBDUABChcLWq6vhEMghoAAjTQddTtG56wPAuBI6gBIEDhWkdNixrBIKgBIEBmUIc+65sxagSPoAaAADX6ur5DX0ftXZ7FrG8Eg6AGgAA1uQbWovZPJqNFjSAQ1AAQoEbf8qyBjlG72jxqaXOHrS5EN4IaAAJktqhTQpz13bElTvc3AkVQA0CA/C3qEIPaYbcp2dcaZ0IZAkVQA0CA2seoQ+v6ltj0BMEjqAEgQO2zvkNrUUsczIHgEdQAEIBWt0euNo+kAbao2e8bQSKoASAA5vahkjQixOVZUsdNT9hGFIEhqAEgAOb4dLzDpoS40H90+lvUdH0jQAQ1AASgfQ116K1pqb1FXUdQI0AENQAEoGmAR1ya/NuIMkaNABHUABAAc4w6aaBBnUjXN4JDUANAAJpbB7Z9qCmVWd8IEkENAAFoNlvU8QMMav+GJ8z6RmAIagAIQHvX98Amk7EzGYJFUANAAPxd3wNsUbPhCYJFUANAAJp9s74HOpmsfcMTghqBIagBIADhmvXNXt8IFkENAAEIe9c3QY0AEdQAEIDmMK+jdrk9amlz93M3QFADQEDCFdTJHWaN06pGIAhqAAhAU2t41lE77Db/MZlMKEMgCGoACIDZoh7ozmQSE8oQHIIaAALQHKYNTyQ2PUFwCGoACEC4ur4lNj1BcAhqAAhAc5iOuZQ6bnrCft/oH0ENAAEw11EPdNa31PFgDlrU6B9BDQABCNfpWVJ71zdBjUAQ1AAQgKYwzvpOcXpnfTNGjUAQ1ADQD8Mwwtr1bc76ZsMTBMLyoF67dq3y8vKUmJiogoIC7dq1q9d7X3zxRdlstm6Pt99+ewgrBhBrWto8Mgzv78PR9Z3mH6NmMhn6Z2lQb9u2TcuWLdNDDz2kvXv3au7cuZo3b55KS0v7fN0777yjiooK/2PKlClDVDGAWGSOT0vSiHCso2Z5FoJgaVCvXr1aixYt0uLFizVt2jStWbNGOTk5WrduXZ+vGzt2rLKzs/0Ph2Pg/8IFgN6Ya6gT4uxy2G0Dfj82PEEwLAtql8ulkpISFRYWdrpeWFio3bt39/naGTNmaNy4cbrqqqv0wgsv9HlvS0uL6urqOj0AIBjmGupwdHtLtKgRHMuCuqqqSm63W1lZWZ2uZ2Vl6fjx4z2+Zty4cdqwYYO2b9+uZ555Ruedd56uuuoq7dy5s9evs2rVKqWnp/sfOTk5Yf0cAKJfs8sjKTwzviX2+kZwBj7YMkA2W+duJMMwul0znXfeeTrvvPP8f541a5bKysr0wx/+UB/5yEd6fM2KFStUVFTk/3NdXR1hDSAoTWFuUbfvTEZQo3+WtagzMzPlcDi6tZ4rKyu7tbL7ctlll+m9997r9Xmn06m0tLRODwAIRlMYl2ZJHbq+aVEjAJYFdUJCggoKClRcXNzpenFxsWbPnh3w++zdu1fjxo0Ld3kA4HcmjJudSO0tapfbozOt7n7uRqyztOu7qKhICxYs0MyZMzVr1ixt2LBBpaWlWrJkiSRvt3V5ebm2bNkiSVqzZo0mTpyo6dOny+Vy6Ze//KW2b9+u7du3W/kxAEQ5c1eyxDB1fScnxMlukzyGVHemNWzvi+hkaVDPnz9f1dXVWrlypSoqKpSfn68dO3YoNzdXklRRUdFpTbXL5dLy5ctVXl6upKQkTZ8+XX/605907bXXWvURAMQAs+s7XC1qu92m1MR41Ta3qq65TWNTw/K2iFI2wzD324kNdXV1Sk9PV21tLePVAALys52H9O0db+nGGWfpR/MvDMt7zv3+8yqradYzS2frogmjwvKeiE6WbyEKAJEu3F3fkpTmW6JV18w2ougbQQ0A/WgOc9e31CGomfmNfhDUANAPc2eysAZ1kneKEC1q9IegBoB+DGrXNydooR8ENQD0Y1C6vpPMMWq6vtE3ghoA+mEecxmuLUQlWtQIHEENAP0wu77DtYWo1D5GXcsYNfpBUANAP9q7vsO3RxTLsxAoghoA+jEYXd/pSSzPQmAIagDoR1Or75jLQZhMVk+LGv0gqAGgH80uj6RBWkfNZDL0g6AGgH6YG54Myqzv5jbF2JELCBJBDQB9MAwj7KdnSe1d3y63Ry1tnrC9L6IPQQ0AfWhp88hs8IZzjDo5wSG7zft7Zn6jLwQ1APTBnPEthbfr22azte9Oxjg1+kBQA0AfzDXUCQ674hzh/ZFpjlPXso0o+kBQA0Af2g/kCP+PS2Z+IxAENQD0wez6DueuZCZ2J0MgCGoA6MNgnJxlaj+Yg65v9I6gBoA+NPnWUIfzLGqTv+ubFjX6QFADQB/au74Hs0VNUKN3BDUA9MHs+g7nGmqTf3kWLWr0gaAGgD40DcLJWaa0RLPrmzFq9I6gBoA+DGrXNxueIAAENQD0ob3rO/zLs9Lp+kYACGoA6MOgdn0nsTwL/SOoAaAPZ4ZgHXUtLWr0IaSgPnz4cLjrAICIZK6jHoxZ3x27vjmTGr0JKagnT56sK6+8Ur/85S915syZcNcEABFjMLu+zaBu8xhq7HBKF9BRSEG9f/9+zZgxQ1/+8peVnZ2tu+++W6+++mq4awMAyw1m13divF0Jcd4fw6ebXGF/f0SHkII6Pz9fq1evVnl5uTZt2qTjx4/r8ssv1/Tp07V69WqdPHky3HUCgCX8LepBCGqbzaaRvlb16SbGqdGzAU0mi4uL04033qinn35a3/ve93Tw4EEtX75cZ599thYuXKiKiopw1QkAlhjMrm+JJVro34CCes+ePVq6dKnGjRun1atXa/ny5Tp48KCef/55lZeX6/rrrw9XnQBgifau7/Cvo5akkSN8LWqCGr0I6Ttv9erV2rRpk9555x1de+212rJli6699lrZ7d7cz8vL0/r16zV16tSwFgsAQ62963twVrOmJyVIousbvQspqNetW6c777xTd9xxh7Kzs3u8Z8KECXryyScHVBwAWK2963twWtRm1zdrqdGbkL7ziouLNWHCBH8L2mQYhsrKyjRhwgQlJCTotttuC0uRAGCVwZz1LXXs+mbWN3oWUl/OOeeco6qqqm7Xa2pqlJeXN+CiACASGIYxqBueSPLP+q6l6xu9CCmoe9tBp6GhQYmJiQMqCAAihcvtkcf3426wgjp9BF3f6FtQXd9FRUWSvGv/Hn74YY0YMcL/nNvt1r/+9S9deOGFYS0QAKzS3GG3sMFensVkMvQmqKDeu3evJG+L+t///rcSEhL8zyUkJOjDH/6wli9fHt4KAcAi5kSyeIdN8Y7BmfU9coRv1jctavQiqKB+4YUXJEl33HGHHnvsMaWlpQ1KUQAQCfxnUQ9Sa1pqH6NmwxP0JqRZ35s2bQp3HQAQcZoHcftQU3vXN7O+0bOAg/qmm27S5s2blZaWpptuuqnPe5955pkBFwYAVjO7vgdrVzKpfXlWo8utVrdn0LrYMXwF/B2Rnp4um83m/31fj2CsXbtWeXl5SkxMVEFBgXbt2hXQ61555RXFxcUxeQ3AoBmKru/UxHj5frQy8xs9CvifiR27u8PV9b1t2zYtW7ZMa9eu1Zw5c7R+/XrNmzdPBw4c0IQJE3p9XW1trRYuXKirrrpKJ06cCEstANBV8yCvoZYkh92mVGec6s606XRTqzJTnIP2tTA8hdTH0tzcrKamJv+fjx49qjVr1ui5554L6n1Wr16tRYsWafHixZo2bZrWrFmjnJwcrVu3rs/X3X333br11ls1a9asUMoHgIC0d30PXlBL7TO/a9mdDD0IKaivv/56bdmyRZJ0+vRpXXLJJXr00Ud1/fXX9xuyJpfLpZKSEhUWFna6XlhYqN27d/f6uk2bNungwYP6+te/HtDXaWlpUV1dXacHAARiKLq+pQ7biLKWGj0IKahff/11zZ07V5L029/+VtnZ2Tp69Ki2bNmixx9/PKD3qKqqktvtVlZWVqfrWVlZOn78eI+vee+99/Tggw/qV7/6leLiAuu1X7VqVafx85ycnIBeBwBDMetb4mAO9C2koG5qalJqaqok6bnnntNNN90ku92uyy67TEePHg3qvcwJaibDMLpdk7w7n9166636xje+oXPPPTfg91+xYoVqa2v9j7KysqDqAxC7moeo65vdydCXkNYcTJ48Wb/73e9044036q9//avuv/9+SVJlZWXAm6BkZmbK4XB0az1XVlZ2a2VLUn19vfbs2aO9e/fq3nvvlSR5PB4ZhqG4uDg999xz+tjHPtbtdU6nU04nkzMABK+pdXCPuDS1n6BFUKO7kFrUDz/8sJYvX66JEyfq0ksv9U/qeu655zRjxoyA3iMhIUEFBQUqLi7udL24uFizZ8/udn9aWpr+/e9/a9++ff7HkiVLdN5552nfvn269NJLQ/koANCr9q7vwV3bnM7uZOhDSP9M/MxnPqPLL79cFRUV+vCHP+y/ftVVV+nGG28M+H2Kioq0YMECzZw5U7NmzdKGDRtUWlqqJUuWSPJ2W5eXl2vLli2y2+3Kz8/v9PqxY8cqMTGx23UACIfmIdjwRJJGJvn2+2Z3MvQg5O++7OxsZWdnd7p2ySWXBPUe8+fPV3V1tVauXKmKigrl5+drx44dys3NlSRVVFSotLQ01BIBYEDMru/EQZ71nU7XN/pgM3o7XLoPjY2N+u53v6u///3vqqyslMfj6fT8oUOHwlZguNXV1Sk9PV21tbUcKgKgT4t//pr+9lalVt10vj57Se+bMA3UX988rrt/UaIZE0bq2aVzBu3rYHgKqUW9ePFivfTSS1qwYIHGjRvX4yxtABjuzHXUg77hCbO+0YeQgvrPf/6z/vSnP2nOHP7lByB6mTuTDXbXd0ayd4z6FGPU6EFIUxlHjRqljIyMcNcCABFlqNZRj0o2txBtVZvb08/diDUhBfU3v/lNPfzww532+waAaDPUXd+Gwe5k6C6kru9HH31UBw8eVFZWliZOnKj4+PhOz7/++uthKQ4ArDRUXd9xDrvSk+JV29yqU00ujeYELXQQUlDfcMMNYS4DACLPmSFaRy15x6lrm1tV00iLGp2F9N0X6MlVADBcGYbhX0c92F3fkjRqRLwOS6ppZEIZOgt5X7zTp0/riSee0IoVK1RTUyPJ2+VdXl4etuIAwCout0duj3ebicHu+paY+Y3ehdSifuONN3T11VcrPT1dR44c0V133aWMjAw9++yz/uMuAWA4M2d8S0PVovYGNS1qdBVSi7qoqEi333673nvvPSUmJvqvz5s3Tzt37gxbcQBglUZfUCc47Ip3DO6hHFKHFjVBjS5C+u577bXXdPfdd3e7ftZZZ3U7thIAhqNmV5skaYRz8FvTUvta6hq6vtFFSEGdmJiourq6btffeecdjRkzZsBFAYDVGlt8E8mGYHxakjJGmCdoMesbnYUU1Ndff71Wrlyp1lbvN5TNZlNpaakefPBBffrTnw5rgQBgBXMN9Qjn4C/Nkjq0qOn6RhchBfUPf/hDnTx5UmPHjlVzc7OuuOIKTZ48Wampqfr2t78d7hoBYMg1mV3fQzCRTJIykr0bRzHrG12F9E/FtLQ0vfzyy3rhhRdUUlIij8ejiy66SFdffXW46wMASzQN0T7fppHM+kYvgg5qj8ejzZs365lnntGRI0dks9mUl5en7OxsGYbBkZcAokJ7i3pour7NMer6M21qdXuGZKY5hoegvhMMw9B//dd/afHixSovL9f555+v6dOn6+jRo7r99tt14403DladADCkhrpFnZYUL7uvnUP3NzoK6p+Kmzdv1s6dO/X3v/9dV155Zafnnn/+ed1www3asmWLFi5cGNYiAWCoDXVQO+w2jRyRoJpGl041tmpsamL/L0JMCKpFvXXrVn3ta1/rFtKS9LGPfUwPPvigfvWrX4WtOACwylB3fUve/b4lxqnRWVBB/cYbb+gTn/hEr8/PmzdP+/fvH3BRAGA1/zrqIWpRS+z3jZ4FFdQ1NTXKysrq9fmsrCydOnVqwEUBgNXMvb6Th2gdtcR+3+hZUEHtdrsVF9f7N63D4VBbW9uAiwIAqzX6ur6ThmhnMon9vtGzoP6paBiGbr/9djmdzh6fb2lpCUtRAGC19hb10AU1+32jJ0EF9W233dbvPcz4BhANGi2YTGaupaZFjY6C+g7ctGnTYNUBABGleYiXZ0kdW9QczIF2bH0DAD1o9Af10LWoR6d4g7q6gWFEtCOoAaAHVrSox6R45/+crCeo0Y6gBoAemGPUQzmZbGyqN6irG11ye4wh+7qIbAQ1APTA3EI0aSgnkyUnyGaT3B6DTU/gR1ADQBdtbo9cbR5JUvIQdn3HOewa7ZtQRvc3TAQ1AHTR1Or2/z5pCINakjIZp0YXBDUAdNHk2+c7zm5TwhCfCz0mlaBGZwQ1AHRhnpyVlOCQzWYb0q/tn/nNEi34ENQA0IU5kSx5CCeSmWhRoyuCGgC6aLJgDbWJoEZXBDUAdOHf53sI11CbCGp0RVADQBf+XcniLej6ZowaXRDUANBFYwstakQOghoAumhutX6Mura5VS1t7n7uRiwgqAGgi8aWoT85y5SeFK94h3dJWFUD24iCoAaAbprNyWQWtKhtNpt/nLqK7m+IoAaAbposOIu6I8ap0RFBDQBdNFq4jlrqENTM/IYiIKjXrl2rvLw8JSYmqqCgQLt27er13pdffllz5szR6NGjlZSUpKlTp+pHP/rREFYLIBZY2fUt0aJGZ9b06/hs27ZNy5Yt09q1azVnzhytX79e8+bN04EDBzRhwoRu9ycnJ+vee+/VBRdcoOTkZL388su6++67lZycrM9//vMWfAIA0ajR6q5vTtBCB5a2qFevXq1FixZp8eLFmjZtmtasWaOcnBytW7eux/tnzJihz372s5o+fbomTpyo//mf/9E111zTZyscAIJlbniSbME6akkak5YoSTped8aSr4/IYllQu1wulZSUqLCwsNP1wsJC7d69O6D32Lt3r3bv3q0rrrii13taWlpUV1fX6QEAfTG3EE2KtyaozxrpDepjp5st+fqILJYFdVVVldxut7Kysjpdz8rK0vHjx/t87dlnny2n06mZM2fqnnvu0eLFi3u9d9WqVUpPT/c/cnJywlI/gOjV3qK2put7/MgkSQQ1vCyfTNb1rFfDMPo9/3XXrl3as2ePfvrTn2rNmjXaunVrr/euWLFCtbW1/kdZWVlY6gYQvRo7nEdthbN8QX2qqdV/NjZil2WTyTIzM+VwOLq1nisrK7u1srvKy8uTJJ1//vk6ceKEHnnkEX32s5/t8V6n0ymn0xmeogHEhGaLl2elJsYrNTFO9WfaVH6qWVOyUi2pA5HBshZ1QkKCCgoKVFxc3Ol6cXGxZs+eHfD7GIahlhZmRgIInwbfoRzJFs36ltpb1eV0f8c8S5dnFRUVacGCBZo5c6ZmzZqlDRs2qLS0VEuWLJHk7bYuLy/Xli1bJEk/+clPNGHCBE2dOlWSd131D3/4Q913332WfQYA0aXN7dGZVo8kKcWiMWpJOntUkt4+Xk9Qw9qgnj9/vqqrq7Vy5UpVVFQoPz9fO3bsUG5uriSpoqJCpaWl/vs9Ho9WrFihw4cPKy4uTuecc46++93v6u6777bqIwCIMuaBHJJ1k8mk9gll5acI6lhnMwzDsLqIoVRXV6f09HTV1tYqLS3N6nIARJjy082a893nlRBn17vfmmdZHetfOqhVf35bN1w4Xmv+e4ZldcB6ls/6BoBI0ugbn7ay21uSzhrFGDW8CGoA6MA/kcyiXclM7Wup2Z0s1hHUANBBYwTM+Jaks31BfbzujNrcHktrgbUIagDooOFMZHR9Z6Y4leCwy+0x2PM7xhHUANBBe9e3tUFtt9s0zr/nN0EdywhqAOjAP5ks0dqgljpuetJkcSWwEkENAB2YZ1GnWDxGLTGhDF4ENQB0ECld35J3dzJJKq2mRR3LCGoA6KB9Mpm1y7MkKS8zWZJ0uLrR4kpgJYIaADpojKAW9aTMFEnSoZMEdSwjqAGgg0jq+p6YOUKSVNXQorozrRZXA6sQ1ADQQaPLG9SpETDrOzUxXmNSnZKkw7SqYxZBDQAdNPhOz7J6ZzLTJHOcuoqgjlUENQB00ODrYo6Erm9JmjTGG9SHCOqYRVADQAfmedRWbyFqMmd+HzrZYHElsApBDQAdNEbI6Vkmc+Y3Xd+xi6AGAB/DMNTgipwtRCUpb0z7GLVhGBZXAysQ1ADg0+Ryy8zCSOn6zhk1Qg67TU0ut07UtVhdDixAUAOAj9ntbbdJSfGR0fWdEGfXhAzveupDVYxTxyKCGgB8/JudJMTJZrNZXE279glljFPHIoIaAHzMGd+RsjTLZK6lPsjM75hEUAOAT0OEzfg2nZuVKkl6v5KgjkUENQD4mEGdkhhvcSWdTc7yLtF690S9xZXACgQ1APiYk8ki4YjLjiaP9Qb1iboW1TZzOEesIagBwKfjZLJIkpYYr+y0REl0f8cighoAfNpb1JEV1JI0xdf9/X4l3d+xhqAGAJ/GCDqLuqspY70Tyt49QYs61hDUAOBTH8lB7WtRv0fXd8whqAHAx2xRp0bIPt8dTfFNKHufmd8xh6AGAB//hicJkTXrW2rv+j5We0b1Z5j5HUsIagDwaYjgru/0EfEam+qUJB1kK9GYQlADgE8kz/qW2sep2fgkthDUAOATyS1qqb37m7XUsYWgBgCf+jPmFqKRGdTmDmXv0aKOKQQ1APiYk7TSImyvb5N5OAdLtGILQQ0Akjwew7+OOi1CW9TmEq0PTjX7x9MR/QhqAJDU6GqTYXh/n5YUmS3qUckJykxJkMTZ1LGEoAYAtY9PxztscsZF7o/G9nFqgjpWRO53IwAMoTrf+HRqYrxsNpvF1fSOcerYQ1ADgNpb1JE6Pm2awszvmENQA4Ckuub2FnUkmzyWFnWsIagBQB1a1EkR3qL27U5WdqpJzS63xdVgKBDUAKD2NdSpzshuUWemOJWRnCDDYOZ3rLA8qNeuXau8vDwlJiaqoKBAu3bt6vXeZ555Rh//+Mc1ZswYpaWladasWfrrX/86hNUCiFZ1ZyL3iMuuJo/xHXlJ93dMsDSot23bpmXLlumhhx7S3r17NXfuXM2bN0+lpaU93r9z5059/OMf144dO1RSUqIrr7xSn/rUp7R3794hrhxAtDFnfUfqGuqOJo1JliQdruIUrVhgaVCvXr1aixYt0uLFizVt2jStWbNGOTk5WrduXY/3r1mzRg888IAuvvhiTZkyRd/5znc0ZcoU/eEPfxjiygFEm/ph1KLOyySoY4llQe1yuVRSUqLCwsJO1wsLC7V79+6A3sPj8ai+vl4ZGRm93tPS0qK6urpODwDoypz1Han7fHdEUMcWy4K6qqpKbrdbWVlZna5nZWXp+PHjAb3Ho48+qsbGRt1yyy293rNq1Sqlp6f7Hzk5OQOqG0B0Gq4tasPc9xRRy/LJZF13ADIMI6BdgbZu3apHHnlE27Zt09ixY3u9b8WKFaqtrfU/ysrKBlwzgOjTcWeySDdh9AjZbN7zs082tFhdDgaZZf90zMzMlMPh6NZ6rqys7NbK7mrbtm1atGiRfvOb3+jqq6/u816n0ymn0zngegFEt+GyjlqSnHEOnT0qSWU1zTpS1aSxqYlWl4RBZFmLOiEhQQUFBSouLu50vbi4WLNnz+71dVu3btXtt9+up556Stddd91glwkgRkT6WdRd5WV6l2gdrmKJVrSztOu7qKhITzzxhDZu3Ki33npL999/v0pLS7VkyRJJ3m7rhQsX+u/funWrFi5cqEcffVSXXXaZjh8/ruPHj6u2ttaqjwAgStQ1m3t9D4+gnuQbpz7EhLKoZ2kfz/z581VdXa2VK1eqoqJC+fn52rFjh3JzcyVJFRUVndZUr1+/Xm1tbbrnnnt0zz33+K/fdttt2rx581CXDyBKtLo9am71bsc5HCaTSR0mlJ0kqKOd5d+RS5cu1dKlS3t8rmv4vvjii4NfEICY0+Abn5aklOEW1LSoo57ls74BwGrmjO8RCQ7FO4bHj0UzqI9WN8ntYYlWNBse35EAMIiG0xpq0/iRSUqIs8vl9ujY6Wary8EgIqgBxLzhtCuZyWG3aeLoEZKYUBbtCGoAMW84nZzVUfuEMpZoRTOCGkDMqx9Gu5J11L6WmhZ1NCOoAcS8Ov+uZMMtqL1d34ermyyuBIOJoAYQ89pb1MOt65vdyWIBQQ0g5vn3+R52Xd/eMeoPTjWrpc1tcTUYLAQ1gJhnzvoebi3qzJQEpTrjZBhSKd3fUYugBhDz2lvUwyuobTab8saw53e0I6gBxLz6luE561tiK9FYQFADiHmnm7xBnT5iGAc1h3NELYIaQMwzg3rUiASLKwkeLeroR1ADiHmnmlySpFHDsEU9ybdEizHq6EVQA4hpLW1uNbm8S5tGJg2/FvVE36YnVQ0t/vXgiC4ENYCYVuvr9rbbht/yLMk7AW5MqlOSdKSKJVrRiKAGENNO+YJ65IgE2e02i6sJjTlOfYgdyqISQQ0gppnj0yOH4fi0KW80E8qiGUENIKadNoN6mB3I0ZG56QlBHZ0IagAxbTgvzTKxRCu6EdQAYlrHMerhalKHTU8Mw7C4GoQbQQ0gpp0exmuoTRNGj5DNJtW3tKmqwWV1OQgzghpATIuGyWTOOIfOHpUkie7vaERQA4hp0dD1LUl5vh3KDrNEK+oQ1ABiWm0UTCaTOoxTs+lJ1CGoAcS04bzPd0ftM79pUUcbghpATIuerm+WaEUrghpAzDIMo33DkyhpUR+pbpLbwxKtaEJQA4hZjS632nyhNtzHqMePTJIzzi5Xm0dlNYxTRxOCGkDMOtXobU074+xKSnBYXM3AOOw2Tcnyzvx+50S9xdUgnAhqADErGrYP7ejcrFRJ0rvHCepoQlADiFnRsNlJR+f5gpoWdXQhqAHErNPN5ozv6Ajqc7N9LWqCOqoQ1ABiVvs+39HR9W22qA+dbJSrzWNxNQgXghpAzDrVGB1rqE3j0hOV6oxTm8dgPXUUIagBxKxo2ZXMZLPZ/N3fjFNHD4IaQMyqjbIxaomZ39GIoAYQs6oaWiRJo5OdFlcSPuexljrqENQAYtaJujOSpLFp0RPU/q5vWtRRg6AGELMq670t6rGpiRZXEj5Ts9MkSaU1Tf5Z7RjeCGoAMamlze3fmWxsavS0qDOSE/xnU79eesriahAOBDWAmHTS15pOcNijajKZJBXkjpIk7TlCUEcDghpATDK7vcekOmWz2SyuJrz8QX2UoI4Glgf12rVrlZeXp8TERBUUFGjXrl293ltRUaFbb71V5513nux2u5YtWzZ0hQKIKpV1vvHpKJpIZpo50RvU+8tOs0NZFLA0qLdt26Zly5bpoYce0t69ezV37lzNmzdPpaWlPd7f0tKiMWPG6KGHHtKHP/zhIa4WQDQ5We+b8R1F49OmSZkpGjkiXi1tHr15rNbqcjBAlgb16tWrtWjRIi1evFjTpk3TmjVrlJOTo3Xr1vV4/8SJE/XYY49p4cKFSk9PH+JqAUSTE3XRN+PbZLfbVDDB26ouoft72LMsqF0ul0pKSlRYWNjpemFhoXbv3h22r9PS0qK6urpODwCojOIWtSQVTGRCWbSwLKirqqrkdruVlZXV6XpWVpaOHz8etq+zatUqpaen+x85OTlhe28Aw5d/DXUUjlFL0sUTMyRJ/zxcrVY349TDmeWTybrOtjQMI6wzMFesWKHa2lr/o6ysLGzvDWD4qozirm9JmpEzUqOTE3S6qVW7D1ZbXQ4GwLKgzszMlMPh6NZ6rqys7NbKHgin06m0tLRODwDouDwrGsU57Lr2/HGSpD/sP2ZxNRgIy4I6ISFBBQUFKi4u7nS9uLhYs2fPtqgqALGgze1RdWN0d31L0icv8Ab1X988rpY2t8XVIFRxVn7xoqIiLViwQDNnztSsWbO0YcMGlZaWasmSJZK83dbl5eXasmWL/zX79u2TJDU0NOjkyZPat2+fEhIS9KEPfciKjwBgGKpudMkwJLstuk7O6uriiRnKSnPqRF2Ldr1bpas/FL7eSgwdS4N6/vz5qq6u1sqVK1VRUaH8/Hzt2LFDubm5krwbnHRdUz1jxgz/70tKSvTUU08pNzdXR44cGcrSAQxj5vh0ZopTDnt07UrWkd1u07Xnj9OmV47o9/uPEdTDlM0wDMPqIoZSXV2d0tPTVVtby3g1EKP+duCEFm/Zo/PPStcf7rvc6nIG1f6y07r+J68owWHXKw9+LGrH5KOZ5bO+AWCotR9vGf2h9eGckbowZ6Rcbo+e+lfPuz4ishHUAGKOf7OTKJ5I1tGdl+dJkn7xz6NMKhuGCGoAMed4rbkrWXSuoe5qXn62stMSVdXQoj/ur7C6HASJoAYQc45UN0qSckePsLiSoRHvsGvBLO8k3Y2vHFaMTU0a9ghqADHnaHWTJGliZrLFlQydWy+ZIGecXW8eq9Nr7P89rBDUAGJKs8utCl/X98TRsRPUo5ITdNNFZ0uSNr1y2OJqEAyCGkBMKa3xtqbTEuM0akS8xdUMrTvmTJTk3amszPffAZGPoAYQUw5XecenJ2Ymh/UAoOHg3KxUzZ2SKY8h/Xz3EavLQYAIagAx5ahvIlksdXt3tMi3VGvrq6WqbWq1uBoEgqAGEFOOmBPJYmTGd1dXnDtGU7NT1ehy65f/Omp1OQgAQQ0gphzp0PUdi2w2m+6+YpIk76SyM61sgBLpCGoAMeWofw11bAa1JH3ygvE6a2SSqhpc2vZamdXloB8ENYCYcabVrWO+pVl5MdqilrwboCz56DmSpNXF76qm0WVxRegLQQ0gZphLs1JjcGlWV5+9OEfTxqWptrlV3//L21aXgz4Q1ABihrk0Ky8Gl2Z1Feew65vXT5ck/fq1Mr16uMbiitAbghpAzPCvoY7h8emOZk7M0M0F3t3Klv7qdR073WxxRegJQQ0gZvynvFaSdF52qsWVRI5vXD9d08alqaqhRXdt2aMmV5vVJaELghpAzNhXdlqSdGHOSEvriCQjEuL0s4UFGp2coDeP1Wnpr15Xq9tjdVnogKAGEBOqGlr0walm2WzS+WenW11ORDl71AhtWDhTifF2vfjOSX31t2/I4+EozEhBUAOICW98cFqSdM6YFKUlxvaM754U5I7S2s9dJIfdpmf2luu7zASPGAQ1gJiwr/S0JOnDZ4+0tI5I9rGpWfr+py+QJG3YeUgbdh60uCJIBDWAGLHvA+9EsgsnjLS2kAj36YKz9bVrp0qSvrPjbW0v+cDiikBQA4h6hmFovzmRjBZ1vz7/kXN011zvKVsPbH9Dz799wuKKYhtBDSDqHaluUm1zqxLi7Jo6jqVZgVgxb5pumnGW3B5DS375ura9Vmp1STGLoAYQ9V4/ekqSlD8+TfEOfuwFwm636XufuUDz8rPlavPoq9v/reW/2c8Z1hbgOxZA1Hv+7UpJ0qxzRltcyfAS77DrJ7depK9cc55sNum3JR/oY4++qN/vP2Z1aTGFoAYQ1c60uvXiO96gvmZ6tsXVDD92u033XDlZ2z4/S1PGpqi60aUvbt2rFc+8wVnWQ4SgBhDVdh+sUqPLrey0RJ1/FhudhOqSvAz96Ytz9cWPTZbNJm19tUyfe+JfbDk6BAhqAFHtuTe9M5YLp2fF/IlZA5UQZ1dR4XnacuclSkuMU8nRU1ryy9flamPL0cFEUAOIWm6Pob+95QvqD9HtHS5zp4zRpjsuUVK8QzvfPamv/Ha/DIMtRwcLQQ0gar12pEZVDS6lJcbp0kkZVpcTVQpyR+mnCwoUZ7fp//Yd04/+9p7VJUUtghpA1Nr8yhFJ0rXnj2NZ1iC44twx+s6N50uSHv/7e3rqX6y1Hgx85wKISkeqGvXXA8clSYsuz7O4muh1y8U5+sJHz5Ekfe3Zf2vN396lGzzMCGoAUWnjK4dlGNKV543RlCx2IxtMD1xznu650hvWa/72nv7nyX/pwLE6i6uKHgQ1gKhT1dCip/eUSZLumjvJ4mqin81m01eumapv3ZCvBIddr7xfret+vEv3PvW63qogsAeKoAYQdb75xwM60+rRBWensxvZEPqfy3L19y9foU9eME6GIf3xjQrNe2yXHvjtflU3tFhd3rBlM2JsMKGurk7p6emqra1VWlqa1eUACLPn3z6hOzfvkd0m/e6eObqA07Is8VZFnf73hff1pzcqJElpiXH6zk3n65MXjLe4suGHFjWAqFFZd0b/79n/SPJOICOkrTNtXJp+cutF2v6FWfrQuDTVnWnTvU/t1QO/3a/GFnYzCwZBDSAqVNaf0Wd/9k8dqz2jiaNHqOjj51ldEiQV5Gbo/+6do/t8W48+vecDffLHL+uND05bXdqwQdc3gGHNMAw9/3alvvGHAyqtadL49ET9+vOzNGH0CKtLQxf/PFSt+7ftU0XtGdlt0ucuzdX9Hz9XGckJVpcW0QhqAMPOqUaXXjtSo1cP12j3wWod8M0sHp+eqK2fv0y5o5MtrhC9Od3k0v/73X/0R9/YdWK8XdeeP07/ffEEXTxxFPux94CgBhDx3B5Duw9W6bk3T+jVwzV650R9p+cTHHbdeXmell55jtIS4y2qEsH4x8FqfXvHAf2nvH351qTMZM2/OEc3XXS2xqQ6LawushDUACJW/ZlW/eKfR/WLfxxVRe2ZTs9NHpuiS/IydGlehmadM1pjUxMtqhKhMgxD+8pOa9trZfr9/mNqcnnPt46z2zR7cqaumjpWF5ydrkmZKUpLiovZ1rblQb127Vr94Ac/UEVFhaZPn641a9Zo7ty5vd7/0ksvqaioSG+++abGjx+vBx54QEuWLAn46xHUQOSraXRp8yuHtXn3EdWd8c4QTk+K13UXjNNHpmRq5sQMZabQ4oomDS1t+uP+Y/r1a2XaV3a62/POOLtGJydodIpTGckJGp2SoNHJCcpIdvp/n52eqHPGpCgx3jH0H2AQWRrU27Zt04IFC7R27VrNmTNH69ev1xNPPKEDBw5owoQJ3e4/fPiw8vPzddddd+nuu+/WK6+8oqVLl2rr1q369Kc/HdDXJKiByNTscuv10lN6dm+5/vRGhZpbva2rc8Yk6wsfnaxPXjAu6n4Ao2fvVzbo72+d0K73qvReZb1O1AW+WYrNJk3IGKEpY1N0blaqzstO1bRxacrLTB62B7NYGtSXXnqpLrroIq1bt85/bdq0abrhhhu0atWqbvd/9atf1e9//3u99dZb/mtLlizR/v379Y9//COgrxnOoP5Pea1Ka5rU6vao1W2oze1Rq8f7q6vNo5Y2j1ra3DrT6pHHMBRnt8lht8thlxx2u+/PvofN+2ucwya7zdb5ObtNcXa77L30+vT2P7Cv/7NGL6/q7TW9f43gv316/xq9v1evrwljvW6PoZY2j860ujv9ahhSvMP8/2NXvN0mh8OmeLtdcQ7vtTi79/9ZvMPu+/9lkyHJYxgyDO+vZr0drxm+Ys3fm88ZkjweQ2da3Wpu9X4Pudo8io+zKcFhV7zvkRBnV4LDpoS49j/bZJOhfr6uIbW0ubt93jaP9/vU/EyOLp8pzm6Tw/xv4HvObrfJ7fGotc2Qy+3x/X3w/p3o+HtXW+fnXG6PWts8qjvTqqoGlw5XNcrtaf//M318mu69crKumZ4te2/f/IgJTa42VTe4VN3oUnVDi+9Xl2oa239f3diiD04163RTa4/vkeCw66xRSRqXnqgUZ5yc8Q4547x/Z+I7/GyOc9jljLMrMd6hRPPXeIfiHXbZbJJNGvLvybgh+0pduFwulZSU6MEHH+x0vbCwULt37+7xNf/4xz9UWFjY6do111yjJ598Uq2trYqP7z6JpKWlRS0t7f8aq62tleQN7IHa8Lf/6Hf7jg34fQB4ZaYk6CNTxuiGGeM1Y4J3BnBDQ33/L0TUS4+T0tPtmpSeJCmpx3sMw1B1g0sHTzbo4MkGvVfZoHeP1+u9yno1NXl0sKlBB8sHXsu+hz+uuDC1zlNTU/sde7csqKuqquR2u5WVldXpelZWlo4fP97ja44fP97j/W1tbaqqqtK4ceO6vWbVqlX6xje+0e16Tk7OAKoHMBjKJO2V9JjVhQB9GL0mfO8VSO+uZUFt6vovCcMw+vzXRU/393TdtGLFChUVFfn/7PF4VFNTo9GjR0fEDMK6ujrl5OSorKxsWI+Z8zkiS7R8Dil6PgufI7JEyudITe3/CFbLgjozM1MOh6Nb67mysrJbq9mUnZ3d4/1xcXEaPbrnE3KcTqeczs6zQ0eOHBl64YMkLS1tWH/Tm/gckSVaPocUPZ+FzxFZhsPnsGwKXEJCggoKClRcXNzpenFxsWbPnt3ja2bNmtXt/ueee04zZ87scXwaAIDhztK56kVFRXriiSe0ceNGvfXWW7r//vtVWlrqXxe9YsUKLVy40H//kiVLdPToURUVFemtt97Sxo0b9eSTT2r58uVWfQQAAAaVpWPU8+fPV3V1tVauXKmKigrl5+drx44dys3NlSRVVFSotLTUf39eXp527Nih+++/Xz/5yU80fvx4Pf744wGvoY5ETqdTX//617t1zw83fI7IEi2fQ4qez8LniCzD6XNYvjMZAADo3fDcpgUAgBhBUAMAEMEIagAAIhhBDQBABCOoLfTtb39bs2fP1ogRI3rdhKW0tFSf+tSnlJycrMzMTH3xi1+Uy+Ua2kKD9O677+r6669XZmam0tLSNGfOHL3wwgtWlxWSP/3pT7r00kuVlJSkzMxM3XTTTVaXNCAtLS268MILZbPZtG/fPqvLCcqRI0e0aNEi5eXlKSkpSeecc46+/vWvR/zfB8l7nG9eXp4SExNVUFCgXbt2WV1S0FatWqWLL75YqampGjt2rG644Qa98847Vpc1YKtWrZLNZtOyZcusLqVXBLWFXC6Xbr75Zn3hC1/o8Xm3263rrrtOjY2Nevnll/XrX/9a27dv15e//OUhrjQ41113ndra2vT888+rpKREF154oT75yU/2uod7pNq+fbsWLFigO+64Q/v379crr7yiW2+91eqyBuSBBx7Q+PHjrS4jJG+//bY8Ho/Wr1+vN998Uz/60Y/005/+VF/72tesLq1P27Zt07Jly/TQQw9p7969mjt3rubNm9dp6elw8NJLL+mee+7RP//5TxUXF6utrU2FhYVqbGy0urSQvfbaa9qwYYMuuOACq0vpmwHLbdq0yUhPT+92fceOHYbdbjfKy8v917Zu3Wo4nU6jtrZ2CCsM3MmTJw1Jxs6dO/3X6urqDEnG3/72NwsrC05ra6tx1llnGU888YTVpYTNjh07jKlTpxpvvvmmIcnYu3ev1SUN2Pe//30jLy/P6jL6dMkllxhLlizpdG3q1KnGgw8+aFFF4VFZWWlIMl566SWrSwlJfX29MWXKFKO4uNi44oorjC996UtWl9QrWtQR7B//+Ify8/M7tYCuueYatbS0qKSkxMLKejd69GhNmzZNW7ZsUWNjo9ra2rR+/XplZWWpoKDA6vIC9vrrr6u8vFx2u10zZszQuHHjNG/ePL355ptWlxaSEydO6K677tIvfvELjRgxwupywqa2tlYZGRlWl9Er8zjfrsfz9nWc73BhHhkcyf/9+3LPPffouuuu09VXX211Kf2y/PQs9K6nYz1HjRqlhISEiO1GttlsKi4u1vXXX6/U1FTZ7XZlZWXpL3/5S0QehtKbQ4cOSZIeeeQRrV69WhMnTtSjjz6qK664Qu++++6w+uFkGIZuv/12LVmyRDNnztSRI0esLiksDh48qB//+Md69NFHrS6lV6Ec5zscGIahoqIiXX755crPz7e6nKD9+te/1uuvv67XXnvN6lICQos6zB555BHZbLY+H3v27An4/Xo6itPo5yjQwRDo5zIMQ0uXLtXYsWO1a9cuvfrqq7r++uv1yU9+UhUVFUNa80A+h8fjkSQ99NBD+vSnP62CggJt2rRJNptNv/nNbyz+FF6BfpYf//jHqqur04oVK6wuuUeh/J05duyYPvGJT+jmm2/W4sWLLao8cMEe5xvp7r33Xr3xxhvaunWr1aUEraysTF/60pf0y1/+UomJiVaXExBa1GF277336r//+7/7vGfixIkBvVd2drb+9a9/dbp26tQptba29noU6GAJ9HM9//zz+uMf/6hTp075j45bu3atiouL9fOf/1wPPvjgUJTbq0A/R319vSTpQx/6kP+60+nUpEmTImYSUKCf5Vvf+pb++c9/dtvTeObMmfrc5z6nn//854NZZr+C/Ttz7NgxXXnllZo1a5Y2bNgwyNUNTCjH+Ua6++67T7///e+1c+dOnX322VaXE7SSkhJVVlZ2Gopzu93auXOn/vd//1ctLS1yOBwWVtgdQR1mmZmZyszMDMt7zZo1S9/+9rdVUVGhcePGSfIe6+l0Ood8vDfQz9XU1CRJsts7d9bY7XZ/K9VKgX6OgoICOZ1OvfPOO7r88sslSa2trTpy5Ij/0BirBfpZHn/8cX3rW9/y//nYsWO65pprtG3bNl166aWDWWJAgvk7U15eriuvvNLfw9H1+yzSdDzO98Ybb/RfN4eHhhPDMHTffffp2Wef1Ysvvqi8vDyrSwrJVVddpX//+9+drt1xxx2aOnWqvvrVr0ZcSEsEtaVKS0tVU1Oj0tJSud1u/7rWyZMnKyUlRYWFhfrQhz6kBQsW6Ac/+IFqamq0fPly3XXXXRF70PmsWbM0atQo3XbbbXr44YeVlJSkn/3sZzp8+LCuu+46q8sLWFpampYsWaKvf/3rysnJUW5urn7wgx9Ikm6++WaLqwvOhAkTOv05JSVFknTOOecMqxbRsWPH9NGPflQTJkzQD3/4Q508edL/XHZ2toWV9a2oqEgLFizQzJkz/b0AHY/zHS7uuecePfXUU/q///s/paam+nsJ0tPTlZSUZHF1gUtNTe02rp6cnKzRo0dH7ni7lVPOY91tt91mSOr2eOGFF/z3HD161LjuuuuMpKQkIyMjw7j33nuNM2fOWFd0AF577TWjsLDQyMjIMFJTU43LLrvM2LFjh9VlBc3lchlf/vKXjbFjxxqpqanG1VdfbfznP/+xuqwBO3z48LBcnrVp06Ye/74Mhx9jP/nJT4zc3FwjISHBuOiii4blkqbe/ttv2rTJ6tIGLNKXZ3HMJQAAESyyB3gAAIhxBDUAABGMoAYAIIIR1AAARDCCGgCACEZQAwAQwQhqAAAiGEENAEAEI6gBBOyjH/2oli1bZnUZQEwhqIEYdfvtt/uPkYyPj9ekSZO0fPlyNTY29vqaZ555Rt/85jeHsEoAHMoBxLBPfOIT2rRpk1pbW7Vr1y4tXrxYjY2NWrduXaf7WltbFR8fr4yMDIsqBWIXLWoghjmdTmVnZysnJ0e33nqrPve5z+l3v/udHnnkEV144YXauHGjJk2aJKfTKcMwunV9t7S06IEHHlBOTo6cTqemTJmiJ5980v/8gQMHdO211yolJUVZWVlasGCBqqqqLPikwPBFUAPwS0pKUmtrqyTp/fff19NPP63t27f7j2DtauHChfr1r3+txx9/XG+99ZZ++tOf+o/RrKio0BVXXKELL7xQe/bs0V/+8hedOHFCt9xyy1B9HCAq0PUNQJL06quv6qmnntJVV10lSXK5XPrFL36hMWPG9Hj/u+++q6efflrFxcW6+uqrJUmTJk3yP79u3TpddNFF+s53vuO/tnHjRuXk5Ojdd9/VueeeO4ifBogetKiBGPbHP/5RKSkpSkxM1KxZs/SRj3xEP/7xjyVJubm5vYa0JO3bt08Oh0NXXHFFj8+XlJTohRdeUEpKiv8xdepUSdLBgwfD/2GAKEWLGohhV155pdatW6f4+HiNHz9e8fHx/ueSk5P7fG1SUlKfz3s8Hn3qU5/S9773vW7PjRs3LrSCgRhEUAMxLDk5WZMnTw7pteeff748Ho9eeuklf9d3RxdddJG2b9+uiRMnKi6OHzVAqOj6BhCSiRMn6rbbbtOdd96p3/3udzp8+LBefPFFPf3005Kke+65RzU1NfrsZz+rV199VYcOHdJzzz2nO++8U2632+LqgeGDoAYQsnXr1ukzn/mMli5dqqlTp+quu+7yb5gyfvx4vfLKK3K73brmmmuUn5+vL33pS0pPT5fdzo8eIFA2wzAMq4sAAAA945+1AABEMIIaAIAIRlADABDBCGoAACIYQQ0AQAQjqAEAiGAENQAAEYygBgAgghHUAABEMIIaAIAIRlADABDB/j+PjytCtWb1RgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(residuals,kind=\"kde\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "1eaf7e3c-abf4-41f8-b4c7-1d79b282b6b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f32bc216470>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## SCatter plot with predictions and residual\n",
    "##uniform distribution\n",
    "plt.scatter(y_pred,residuals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "54a269c1-c347-483e-a8d1-03b9f70c7e43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59f052b5-5a80-4688-b536-38ef05d00da8",
   "metadata": {},
   "source": [
    "## Pickling \n",
    "Python pickle module is used for serialising and de-serialising a Python object structure. Any object in Python can be pickled so that it can be saved on disk. What pickle does is that it “serialises” the object first before writing it to file. Pickling is a way to convert a python object (list, dict, etc.) into a character stream. The idea is that this character stream contains all the information necessary to reconstruct the object in another python script."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "abe35992-304f-4548-b3a2-bb180a1cf656",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(regression,open('regressor.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "20f1c387-931a-4197-8f51-b3860a5e15a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=pickle.load(open('regressor.pkl','rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "39f4ff2b-c076-4d2d-ae9f-eb31c2438d5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.00397485, 2.58011486, 2.3489077 , ..., 3.09003708, 0.79152007,\n",
       "       2.04477012])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40c00873-284d-4082-be93-f27161871030",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
